code: def run_lightning():
      import pytorch_lightning as pl
  
      from ray.train.lightning import (
          RayDDPStrategy,
          RayDeepSpeedStrategy,
          RayFSDPStrategy,
          RayLightningEnvironment,
          RayTrainReportCallback,
          prepare_trainer,
      )
  
      def train_func():
          # Test Lighting utilites
          strategy = RayFSDPStrategy()
          strategy = RayDeepSpeedStrategy()
          strategy = RayDDPStrategy()
          ray_environment = RayLightningEnvironment()
          report_callback = RayTrainReportCallback()
  
          trainer = pl.Trainer(
              devices="auto",
              accelerator="auto",
              strategy=strategy,
              plugins=[ray_environment],
              callbacks=[report_callback],
          )
          trainer = prepare_trainer(trainer)
  
      trainer = TorchTrainer(
          train_func,
          scaling_config=ScalingConfig(num_workers=2, use_gpu=False),
      )
  
      trainer.fit()
  
file_path: ray/python/ray/train/tests/test_train_usage.py
imports: import pytest
  import torch
  import ray
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: run_lightning() without return types